CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing

Marco Avvenuti (), Stefano Cresci (), Fabio Del Vigna (), Tiziano Fagni () and Maurizio Tesconi ()
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Marco Avvenuti: University of Pisa
Stefano Cresci: National Research Council (CNR)
Fabio Del Vigna: National Research Council (CNR)
Tiziano Fagni: National Research Council (CNR)
Maurizio Tesconi: National Research Council (CNR)

Information Systems Frontiers, 2018, vol. 20, issue 5, No 8, 993-1011

Abstract: Abstract Natural disasters, as well as human-made disasters, can have a deep impact on wide geographic areas, and emergency responders can benefit from the early estimation of emergency consequences. This work presents CrisMap, a Big Data crisis mapping system capable of quickly collecting and analyzing social media data. CrisMap extracts potential crisis-related actionable information from tweets by adopting a classification technique based on word embeddings and by exploiting a combination of readily-available semantic annotators to geoparse tweets. The enriched tweets are then visualized in customizable, Web-based dashboards, also leveraging ad-hoc quantitative visualizations like choropleth maps. The maps produced by our system help to estimate the impact of the emergency in its early phases, to identify areas that have been severely struck, and to acquire a greater situational awareness. We extensively benchmark the performance of our system on two Italian natural disasters by validating our maps against authoritative data. Finally, we perform a qualitative case-study on a recent devastating earthquake occurred in Central Italy.

Keywords: Crisis mapping; Word embeddings; Geoparsing; Online social networks; Social media; Big data (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10796-018-9833-z

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